13 citations · 69 across the 11 of their papers we have counts for
6 papers · 1 filter
Applications of Deep Learning to physics workflows
Manan Agarwal, Jay Alameda, Jeroen Audenaert +65
Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing…
Hierarchical Graph Neural Networks for Particle Track Reconstruction
Ryan Liu, Paolo Calafiura, Steven Farrell +3
We introduce a novel variant of GNN for particle tracking called Hierarchical Graph Neural Network (HGNN). The architecture creates a set of higher-level representations which corr…
Generative Machine Learning for Detector Response Modeling with a Conditional Normalizing Flow
Allison Xu, Shuo Han, Xiangyang Ju +1
In this paper, we explore the potential of generative machine learning models as an alternative to the computationally expensive Monte Carlo (MC) simulations commonly used by the L…
Track Reconstruction using Geometric Deep Learning in the Straw Tube Tracker (STT) at the PANDA Experiment
Adeel Akram, Xiangyang Ju
The PANDA (anti-Proton ANnihilation at DArmstadt) experiment at the Facility for Anti-proton and Ion Research is going to study strong interactions at the scale at which quarks are…
Hyperparameter Optimization of Generative Adversarial Network Models for High-Energy Physics Simulations
Vincent Dumont, Xiangyang Ju, Juliane Mueller
The Generative Adversarial Network (GAN) is a powerful and flexible tool that can generate high-fidelity synthesized data by learning. It has seen many applications in simulating e…
Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers
V Hewes, Adam Aurisano, Giuseppe Cerati +14
This paper presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC). GNNs are still…